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Genetic Algorithms
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The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.read more
Citations
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Cooperative coevolution of artificial neural network ensembles for pattern classification
TL;DR: This paper proposes a general framework for designing neural network ensembles by means of cooperative coevolution, and applies the proposed model to ten real-world classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set.
Journal ArticleDOI
Application of genetic algorithms and thermogravimetry to determine the kinetics of polyurethane foam in smoldering combustion
TL;DR: In this paper, the kinetic parameters governing the thermal and oxidative degradation of flexible polyurethane foam are determined using thermogravimetric data and a genetic algorithm using a lumped model of solid mass loss based on Arrhenius-type reaction rates.
Journal ArticleDOI
Dynamical modeling and multi-experiment fitting with PottersWheel
Thomas Maiwald,Jens Timmer +1 more
TL;DR: The comprehensive modeling framework Potters-Wheel (PW) is presented including novel functionalities to satisfy the requirements of modelers in Systems Biology with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks.
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Fault diagnosis in spur gears based on genetic algorithm and random forest
Mariela Cerrada,Mariela Cerrada,Grover Zurita,Diego Cabrera,René-Vinicio Sánchez,Mariano Artés,Chuan Li,Chuan Li +7 more
TL;DR: The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time–frequency domains, which are extracted from vibration signals.
Journal ArticleDOI
A Greedy Randomized Adaptive Search Procedure for Transmission Expansion Planning
TL;DR: In this paper, a greedy randomized adaptive search procedure (GRASP) is applied to solve the transmission network expansion problem, and the best solution over all GRASP iterations is chosen as the result.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Journal ArticleDOI
An Introduction to Genetic Algorithms.
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
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An Introduction to Population Genetics Theory
James F. Crow,Motoo Kimura +1 more
TL;DR: An introduction to population genetics theory, An introduction to Population Genetics Theory, Population Genetics theory, Population genetics theory as discussed by the authors, Population genetics, population genetics, and population genetics theories, Population Genetic Theory